The present invention relates to a system and method for a business tool for automatically generating product demand groups in a retail setting for improving efficiency of demand modeling. This business tool may be stand alone, or may be integrated into a pricing optimization system to provide more effective pricing of products. More particularly, the present auto demand group generator system may identify and group products by attributes into demand groups. ‘Demand groups’, for the purpose of this application, refers to groupings of highly substitutable products. From these generated demand groups the system may generate further business decisions such as price optimizations, product assortments and promotion decisions.
For a business to properly and profitably function there must be proper pricing of the inventory which, over a sustained period, effectively generates more revenue than costs incurred. In order to reach a profitable condition the business is always striving to increase revenue while reducing costs.
One such method to increase revenue is providing a desirable set of products and properly pricing these products or services being sold. Additionally, the use of promotions may generate increased sales which aid in the generation of revenue. Likewise, costs may be decreased by ensuring that only required inventory is shipped and stored. Also, reducing promotion activity reduces costs. Thus, in many instances, there is a balancing between a business activity's costs and the additional revenue generated by said activity. The key to a successful business is choosing the best activities which maximize the profits of the business.
Choosing these profit maximizing activities is not always a clear decision. There may be no readily identifiable result to a particular activity. Other times, the profit response to a particular promotion may be counter intuitive. Thus, generating systems and methods for identifying and generating business activities which achieves a desired business result is a prized and elusive goal.
A number of business decision suites are available to facilitate product inventory, pricing and promotional activity. In these known systems, product demand and elasticity may be modeled to project sales at a given price. The most advanced models include cross elasticity between sales of various products. While these methods of generating prices and promotions may be of great use to a particular business, there are a number of problems with these systems. Particularly, due to the large inventories many retailers have (some retailers carry thousands of products in any given store) the ability to accurately model demand for every product may be computationally prohibitive due to resource and time limitations.
In order to reduce computational requirements when modeling product demands, a number of possible solutions have been attempted. For example, some demand modeling systems may estimate the value of particular variables when running demand models. Particularly, elasticities between weakly interactive products may be estimated, or even eliminated entirely, from the demand functions in an attempt to reduce the computations required. This estimation based solution may reduce computations; however, these efficiency gains may come at the cost of the demand model's accuracy.
Other solutions include partial demand modeling for particular products (as opposed to the entire product inventory), less accurate but simpler modeling functions, and aggregation of products into groups for group modeling, among other solutions. In general, these methods all increase modeling efficiency, but again, there is often a cost associated with said efficiencies. For example, simpler models may result in less accurate models, and modeling for a subset of products may ignore important cross elasticities.
One such method currently utilized to reduce computations involves grouping products and modeling demand for the group rather than for individual products. This method may lose some of the granularity that product level modeling provides dependent upon group content and size. It has been found that groups of highly substitutable products (known as demand groups) function well to minimize computational loads yet achieve accurate modeling data. However, generation of demand models is often a tedious process which requires close review of each product by a knowledgeable individual. This is time consuming and costly, and may pose a significant hang-up for price optimizations. This is further exasperated when new products are introduced which the individual has little knowledge of.
It is therefore apparent that an urgent need exists for a system for semi-automatically generating demand groups given a particular product set for demand modeling. This auto demand model generator enables rapid and efficient generation of demand groups for the improvement of demand modeling efficiency. When coupled to a pricing optimization system, the auto demand group generator may generate pricing for the given products more efficiently. This auto demand group generator provides businesses with an advanced competitive tool to greatly increase business profitability.